An exemplary system and method are disclosed for a handheld or hand-enclosed instrument configured with ultra-wideband localization using unidirectional messaging protocol based on time difference of arrival (TDoA) measurements that can be used as inputs, in a realtime control loop, to a software application that uses the handheld or hand-enclosed instrument as an input user interface. The handheld or hand-enclosed instrument can be readily employed for UI in large work areas (e.g., 2D), such as a whiteboard or wallboard, with sub-millimeter resolution and sub-millisecond latency. The handheld or hand-enclosed instrument can be readily employed in a UI device for non-conforming 3D workspace such as a sculpting instrument, in hand-enclosed game interface, as a remote medical instrument, among others described herein.

Systems are provided for determining location of tags in an environment. The system includes at least one mobile antenna that moves around the environment. The absolute location associated with the mobile antenna is known and it sends and receives messages including at least one of time of arrival, time of departure, signal strength and angle of arrival. A measurement module performs distance measurement between the at least one mobile antenna and at least one tag in the environment to obtain at least. The distance measurements are associated with at least two locations of mobile antenna. A storage module records the distance measurements at the at least two locations and the at least two locations. A location module computes a location of each of the at least one tags based on the recorded distance measurements at the at least two locations and the recorded locations of the mobile antenna.

An electronic device includes a processor configured to: receive a Radio Frequency (RF) signal of a designated frequency band from an external electronic device by using at least two antennas among the multiple antennas; acquire first angle-of-arrival data of the RF signal, based on at least a part of the RF signal; determine a posture of the electronic device based on tilt information of the electronic device provided from a sensor module; based on the electronic device that is determined to be tilted in the first direction or the second direction; identify a compensation value corresponding to tilt information of the electronic device; acquire second angle-of-arrival data by applying the compensation value to the first angle-of-arrival data; and determine a location of the external electronic device based on the second angle-of-arrival data.

Head-mounted augmented reality (AR) devices can track pose of a wearer's head to provide a three-dimensional virtual representation of objects in the wearer's environment. An electromagnetic (EM) tracking system can track head or body pose. A handheld user input device can include an EM emitter that generates an EM field, and the head-mounted AR device can include an EM sensor that senses the EM field. EM information from the sensor can be analyzed to determine location and/or orientation of the sensor and thereby the wearer's pose. The EM emitter and sensor may utilize time division multiplexing (TDM) or dynamic frequency tuning to operate at multiple frequencies. Voltage gain control may be implemented in the transmitter, rather than the sensor, allowing smaller and lighter weight sensor designs. The EM sensor can implement noise cancellation to reduce the level of EM interference generated by nearby audio speakers.

The present disclosure provides a method by which an electronic device classifies a pose by using a UWB signal. The method of the present disclosure may comprise the steps of: using a trained first CNN model so as to acquire first prediction data for classifying whether a signal corresponding to a UWB CIR is a LOS signal or an NLOS signal on the basis of UWB CIR data; using, if the signal is classified as the LOS signal, a trained second CNN model so as to acquire second prediction data for classifying a pose on the basis of sensor data corresponding to the UWB CIR data; using, if the signal is classified as the NLOS signal, a trained third CNN model so as to acquire third prediction data for classifying the pose on the basis of the sensor data corresponding to the UWB CIR data; using a first filtering method so as to filter at least two from among the first prediction data, the second prediction data, and the third prediction data; and classifying the pose on the basis of the filtered data.